Conventional Tools for Neurodegenerative Disease Diagnosis: History
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Contributor:
Alia Colniță
,
Vlad-Alexandru Toma
,
Ioana Andreea Brezeștean
,
Muhammad Ali Tahir
,
Nicoleta Elena Dina

Conventional approaches in neurodegenerative disease (ND) diagnosis and challenges in clinical routine testing are addressed in order to understand the context of how molecular-based diagnosis techniques can perform in real, in vivo sampling and bioassays for early ND diagnosis.

  • neurodegenerative diseases
  • SERS
  • molecular biology techniques

1. Introduction

The neurodegeneration spectrum takes into account the contribution of several molecular sciences in disease definition and features. However, several canonic neurodegenerations such as Parkinson’s disease (PD) or Alzheimer’s disease (AD) are references in neuroscience as NDs. According to the “neurogenesis hypothesis of depression”, major depressive disorder (MDD) can also be considered a mild neurodegenerative disorder that is caused by changes in the rate of neurogenesis [1][2][3]. Other classes of disorders known as neurodevelopmental diseases such as autism spectrum disorders (ASDs) may induce neurodegeneration in the form of neuronal cell loss, activated microglia and astrocytes, proinflammatory cytokines, oxidative stress, and elevated 8-oxo-guanosine levels [3].
From the medical point of view, it is still not completely understood what neural mechanisms should be monitored; however, the main pathways are sketched by experts. Traditional testing for NDs mainly relies on psychiatric queries, biomedical imaging methods such as computer tomography (CT) or hyperspectral imaging, and usually offers a tardive diagnosis. Invasive tests based on the thorough analysis of cerebrospinal fluid (CSF), such as the common lumbar puncture, are often disliked by patients [4]. Thus, current clinical trials are designed based on the stringent need for non-invasive and early diagnosis of NDs coupled with the drive for efficient mechanisms to probe biomarkers predicting disease onset (prognosis).
To keep track of the recent advancements and breakthroughs in the societal environment, the need for new diagnosis methods for NDs is of extreme importance. Since the 2018 addition of the Alzheimer’s Association’s International Conference (Chicago, IL, USA) [5], new diagnosis recommendations were introduced for rapid screening and prognosis tools, accurate clinical management, and a correct assessing of the genetic implications by participating in clinical trials. The impact and costs that NDs create for public health systems are huge [6]; therefore, there is a crucial need for fast and reliable alternative diagnostic tools.
Many NDs are considered proteinopathies, and there is much interest in tackling their associated pathomechanisms along with the pathophysiological role of relevant biological markers [7]. The most targeted bioanalytes for ND diagnosis are: (i) neurotransmitter-related metabolites present in biofluids (such as CSF), with specific levels in case of neural deterioration, or (ii) biomarkers revealed by the histopathology of brain tissue. The recent improvements in substrate design and rational fabrication expanded the use of SERS-based biosensors in biological and biomedical applications, such as detection of nuclear acids, biomarker monitoring, or medical diagnosis [8][9][10].
Neurotransmitter-related CSF metabolites have been mainly targeted in SERS-based biosensing as they can be detected prior to the cognitive symptoms’ onset. These metabolites are considered useful predictive tools and good biomarker candidates for ND detection having been recently identified and summarized by Wakamatsu et al. [11]: amyloid-β (Aβ) peptides [12][13], total (T) or phosphorylated (P-tau) tau proteins, α-synuclein (αSyn), neurogranin, neuroligin-1, miRNAs, and γ-aminobutyric acid (GABA). Melatonin, serotonin, glutamate, dopamine, norepinephrine, and epinephrine are also of interest [14] and have been detected using SERS biosensors down to picomolar (pM) [15][16][17][18][19] or even attomolar concentrations when using solid-state SERS platforms [20][21].

2. Conventional Approaches for ND Diagnosis

Dementia is considered to be an “umbrella term” to describe a neurodegenerative, progressive condition characterized by “functional” impairment. It is mostly irreversible due to the degeneration of brain cells and their interconnections, which compromises the daily activities of a person’s life. AD is the most prevalent form of dementia and is thought to account for 70–80% of cases in the elderly [22]. Initially, AD manifests with an impairment of recent memory function and attention and continues with failure of language skills, visual–spatial orientation, abstract thinking, and judgment. The diagnosis of AD currently relies on the identification of characteristic clinical signs and can only be confirmed by the distinctive cellular pathology evidence from postmortem examination of the brain [23].
The histological changes described so far involve three aspects: (i) collections of neurofibrillary tangles, (ii) pericellular deposits of amyloid, and (iii) a diffuse loss of neurons. These changes are most frequent in the neocortex, hippocampal formation, amygdala neurons, and basal forebrain nuclei [24]. A mutation of the gene encoding amyloid precursor protein (APP) has emerged as a prominent candidate because of both the significant amyloid deposits in AD and the isolation of a fragment of APP, Aβ, from amyloid plaques [25][26][27][28]. In this light, using enzyme-linked immunosorbent assay (ELISA) or protein immunoblot (Western blot) are considered conventional molecular methods for testing blood or CSF samples for Aβ peptide in AD diagnosis [29][30]. In addition to Aβ, two other proteins were associated with the genetic induction of AD, presenilin 1 and presenilin 2 [31]. Mutations of presenilin 1 and 2 modify the processing of APP, resulting in increased amounts of Aβ42, a particularly toxic form of the Aβ peptide [31][32]. Accumulation of the Aβ42 peptide, in particular, is thought to be a key factor. However, this can be established by immunohistochemistry and Western blot analyses, procedures that are performed in research experimental conditions or on postmortem brain samples. In terms of accuracy–LOD–costs, these methods provide average (immunohistochemistry) to high accuracy (Western blot) but are also expensive and research laboratory dependent.
The second most common neurodegenerative disease is considered to be PD, which predominantly affects the “dopaminergic” neurons located in the substantia nigra part of the brain [33]. It has a relatively long onset period during which symptoms such as tremor at rest, bradykinesia, rigidity of the extremities and neck, and minimal facial expressions [34] are experienced by the patient. Currently, joint genetic and environmental factors (pesticide, herbicide, and heavy metal exposure) are thought to cause the progressive deterioration of these dopaminergic neurons [35].
Besides MRI, the conventional diagnosis of PD relies solely on the clinical signs. Recent advances have suggested a few molecular approaches to detect PD using biomarkers in the blood or CSF [36][37][38]. These analyses involved gene mutations for α-synuclein, Parkin, and DJ-1 genes that were noted as important in rare forms of PD [39][40]. Another proposed method to diagnose PD was the estimation of the dopamine concentration in CSF with chromatography; however, this procedure is inconsistent in terms of results. The measurement of tyrosine hydroxylase activity in different fluids such as plasma, serum, or cerebrospinal fluid has also been considered for PD diagnosis [41][42]. Classical PD diagnosis based on imaging techniques is still the gold-standard procedure, whereas other methods are inconsistent or very expensive [42]. Other time-dependent evolutive diseases are considered such as autism spectrum disorders (ASDs) and major depressive disorders (MDDs).
Autism spectrum disorders (ASD) are developmental disorders that result from an abnormal process of brain development and maturation [43][44]. The underdevelopment of the brain manifests in impairments in social interaction, language, communication and imaginative play, and in the range of interests and activities. Along this line, many therapeutic approaches were adopted in order to ameliorate the autism-related symptoms and to improve the magnitude of social skills in ASD. One of the most popular approaches used in the treatment of autism is the Social StoryTM method [45]. This type of intervention aims at providing ASD individuals with the social information they lack and thus help them to develop appropriate behavior in their social life. Recently published studies suggest a connection between ASD risk genes and certain proteins involved in the synaptic mechanisms (GABA and glutamate receptors such as GRIN2B) or membrane ion channels and genes coding for proteins involved in cell regulation [46]. Furthermore, insights into the neuropathology in ASD have revealed abnormal cellular changes in the limbic system structures, an increased packing density of neurons in the pyramidal layers of the hippocampal and subicular subfields, and a reduction in the density and distribution of GABA receptors [47]. Consistent abnormalities on the long arm of chromosome 15 in the q11-13 region that codes for three isoforms of GABA receptor subunits have also been reported [48]. Moreover, early brain-derived neurotrophic factor (BDNF) hyperactivity may play an etiological role in autism as serum BDNF levels positively correlate with the cortical BDNF levels and have increased values in autism [49]. Currently, ASD diagnostic and clinical trials selection criteria are heavily guided by the Diagnostic and Statistical Manual of Mental Disorders or behavioral diagnostic scales [50]. Furthermore, the genetic underpinnings of ASD had a successful diagnostic rate of up to 38% by introducing molecular biology methods (DNA sequencing, transcriptomics, and polymerase chain reaction—PCR, RT-PCR) [51][52][53]. Moreover, the investigation of several molecular panels (e.g., RAREs, blood exosomes with brain trophic factors, miRNAs, or neurotransmitters) that are closely related to ASD is a current diagnostic procedure. Unfortunately, these molecular-based protocols come with a high financial impact.
Structural magnetic neuroimaging methods such as proton magnetic resonance spectroscopy (1H-MRS) can successfully quantify in vivo the low-molecular-weight molecular metabolites related to ASD in children, such as creatinine, phosphocreatine (Cr + PCr), N-acetylaspartate (NAA), choline, myo-inositol, and lactate at clinical and sub-clinical detection levels. The pros and cons of these are detailed in the review by Ford et al. [54]. On the other hand, functional MR imaging offers valuable information on aspects regarding social skills and independent domestic activities management [55]. It is also worth mentioning an attempt to develop a complementary spectroscopic diagnostic tool based on a chemometric model that relies on infrared (IR) spectroscopic data for ASD detection in children and teenagers 4–17 years old [56]. Principal component analysis (PCA)- and partial least squares discriminant analysis (PLS-DA)-based models analyzing FT-IR spectra recorded on blood serum samples have shown a clear separation of ASD individuals from healthy control samples.
On the other hand, MDD is characterized by long-lasting desensitization of 5-HT1A autoreceptors in the dorsal raphe [57]. Other changes have also been detected, such as changes in the neurotrophins NT3, NT4, and BDNF, as well as increased corticosterone and IL34 levels in the blood during MDD [58][59]. Brain epitranscriptomic studies revealed a series of downregulated miRNAs (miR-96, 414, 182, 193, 298, and 429) in MDD [60] that participate in the alterations of the gene expression network in the hippocampus, amygdala, and prefrontal cortex. In addition, recent studies [61][62][63] have emphasized miR323a’s important role in the development of MDD and the possibility to exploit it as a therapeutic target. Moreover, brain miRs involved in MDD are conveyed intercellularly through extracellular vesicles (EVs) generated in multivesicular bodies (MVB), and EVs pass the blood–brain barrier and circulate in the blood flow with their cellular-origin tags (L1CAM for neurons (unspecific), GFAP for astrocytes, and CNP for oligodendrocytes) [64][65][66]. Based on experimental studies with animal models, EV isolation from blood plasma in MDD subjects can be developed as an accurate predictive test based on analyzing miR323a as a specific biomarker.
Thus, improving molecular-based diagnosis techniques will definitely add value to the current advances in terms of diagnosis, prognosis, and therapeutic strategies and could pinpoint the biological signature of NDs in clinical settings. Table 1 summarizes the main techniques currently used for ND diagnosis by highlighting their advantages and limitations in clinical practice.
Table 1. Techniques used for ND diagnosis with their known assets and limitations from practical point of view [67][68].
  Clinical Examples Advantages Disadvantages Current Use
Imaging techniques Magnetic resonance imaging (MRI) Label based
Reliable
High resolution
Wide collection of fluorophores or contrast agents		 Incapable of differential diagnosis due to chemical contrast limitations
Current need for tissue-specific labeling
Toxicity due to dye usage		 In vivo
Optical coherence tomography (OCT) In vivo
Two-photon excited fluorescence (TPEF) In vivo
(limited)
Photoacoustic imaging High sensitivity
Specificity
Rapid output		 Costs due to specific labels
Specific absorption limitations of dyes in practice
Toxicity due to dye usage		 In vivo
Confocal fluorescence microscopy
Spectroscopic techniques IR microscopy Label free
Provides chemical contrast due to molecular vibrations specific selectivity		 Long wavelength → low spatial resolution
Interference of water absorption specific to biological samples
Tissue depth penetration is limited		 Ex vivo
Raman scattering Label free
Complex matrices as samples with minimal preparation required (biofluids, cells, tissues, etc.)
No interference from water (intrinsic to samples)
Selective tool for NDs differential diagnostics
Imaging of (bio-)molecular distribution
Specific molecular fingerprinting spectral output
Clinical adapted portable setups: optical fibers with possibility to integrate with cannulas, endoscopes, catheters		 Time consuming when using mapping technique of large tissular areas
Low efficiency of the scattering process translated into inherently weak Raman signals
Potentially destructive to the sample due to long laser exposure times		 In vivo
Hyperspectral Raman imaging Able to show the amyloid plaques, neuritic plaques, or neurofibrillary tangles and is able to distinguish tissue components for margin determinations
Surface-enhanced Raman scattering Label free
No need for staining
High chemical specificity
Combined with cryo-sampling of brain tissue		 Signal enhancement is local, mainly due to the molecules in close contact with the metallic nanostructures In vivo
Label based
When combined with spatially offset (SORS) the detection is possible within the skull
Bio-barcode assays for differential PD diagnosis
Reduced time
Multiplexing capacity		 Silver nanoparticles (AgNPs) are shown to alter neurotransmitters in in vivo conditions (rats)
Reliant on costly antibodies specific to NDs
Molecular Biology techniques ELISA/Western blot Label based
High accuracy
Suitable for routine protocols (predictive and diagnosis screening)		 Costly due to highly specific reagents required, invasive, and laboratory dependent
Time consuming when including bioinformatics protocols		 Ex vivo
Immunohistochemistry
Genomics
(PCR, RT-PCR, DNA sequencing, (epi)transcriptomics)

This entry is adapted from the peer-reviewed paper 10.3390/bios13050499

References

  1. Bewernick, B.H.; Schlaepfer, T.E. Chronic depression as a model disease for cerebral aging. Dialogues Clin. Neurosci. 2013, 15, 77–85.
  2. Jęśko, H.; Cieślik, M.; Gromadzka, G.; Adamczyk, A. Dysfunctional proteins in neuropsychiatric disorders: From neurodegeneration to autism spectrum disorders. Neurochem. Int. 2020, 141, 104853.
  3. Kern, J.K.; Geier, D.A.; Sykes, L.K.; Geier, M.R. Evidence of neurodegeneration in autism spectrum disorder. Transl. Neurodegener. 2013, 2, 17.
  4. Koníčková, D.; Menšíková, K.; Tučková, L.; Hényková, E.; Strnad, M.; Friedecký, D.; Stejskal, D.; Matěj, R.; Kaňovský, P. Biomarkers of Neurodegenerative Diseases: Biology, Taxonomy, Clinical Relevance, and Current Research Status. Biomedicines 2022, 10, 1760.
  5. Turner, R.S.; Stubbs, T.; Davies, D.A.; Albensi, B.C. Potential New Approaches for Diagnosis of Alzheimer’s Disease and Related Dementias. Front. Neurol. 2020, 11, 496.
  6. Nichols, E.; Steinmetz, J.D.; Vollset, S.E.; Fukutaki, K.; Chalek, J.; Abd-Allah, F.; Abdoli, A.; Abualhasan, A.; Abu-Gharbieh, E.; Akram, T.T.; et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health 2022, 7, e105–e125.
  7. Delaby, C.; Lehmann, S. Proteinopathies: Molecular mechanisms and diagnostic perspectives. J. Neural Transm. 2022, 129, 129–130.
  8. Li, P.; Long, F.; Chen, W.; Chen, J.; Chu, P.K.; Wang, H. Fundamentals and applications of surface-enhanced Raman spectroscopy–based biosensors. Curr. Opin. Biomed. Eng. 2020, 13, 51–59.
  9. Dina, N.E.; Tahir, M.A.; Bajwa, S.Z.; Amin, I.; Valev, V.K.; Zhang, L. SERS-based antibiotic susceptibility testing: Towards point-of-care clinical diagnosis. Biosens. Bioelectron. 2022, 219, 114843.
  10. Tahir, M.A.; Dina, N.E.; Cheng, H.; Valev, V.K.; Zhang, L. Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis. Nanoscale 2021, 13, 11593–11634.
  11. Wakamatsu, K.; Chiba, Y.; Murakami, R.; Miyai, Y.; Matsumoto, K.; Kamada, M.; Nonaka, W.; Uemura, N.; Yanase, K.; Ueno, M. Metabolites and Biomarker Compounds of Neurodegenerative Diseases in Cerebrospinal Fluid. Metabolites 2022, 12, 343.
  12. Hilal, S.; Wolters, F.J.; Verbeek, M.M.; Vanderstichele, H.; Ikram, M.K.; Stoops, E.; Ikram, M.A.; Vernooij, M.W. Plasma amyloid-β levels, cerebral atrophy and risk of dementia: A population-based study. Alzheimers Res. Ther. 2018, 10, 63.
  13. Hao, N.; Wang, Z.; Liu, P.; Becker, R.; Yang, S.; Yang, K.; Pei, Z.; Zhang, P.; Xia, J.; Shen, L.; et al. Acoustofluidic multimodal diagnostic system for Alzheimer's disease. Biosens. Bioelectron. 2022, 196, 113730.
  14. Hook, V.; Kind, T.; Podvin, S.; Palazoglu, M.; Tran, C.; Toneff, T.; Samra, S.; Lietz, C.; Fiehn, O. Metabolomics Analyses of 14 Classical Neurotransmitters by GC-TOF with LC-MS Illustrates Secretion of 9 Cell-Cell Signaling Molecules from Sympathoadrenal Chromaffin Cells in the Presence of Lithium. ACS Chem. Neurosci. 2019, 10, 1369–1379.
  15. Moody, A.S.; Sharma, B. Multi-metal, Multi-wavelength Surface-Enhanced Raman Spectroscopy Detection of Neurotransmitters. ACS Chem. Neurosci. 2018, 9, 1380–1387.
  16. Siek, M.; Kaminska, A.; Kelm, A.; Rolinski, T.; Holyst, R.; Opallo, M.; Niedziolka-Jonsson, J. Electrodeposition for preparation of efficient surface-enhanced Raman scattering-active silver nanoparticle substrates for neurotransmitter detection. Electrochim. Acta 2013, 89, 284–291.
  17. Tang, L.; Li, S.; Han, F.; Liu, L.; Xu, L.; Ma, W.; Kuang, H.; Li, A.; Wang, L.; Xu, C. SERS-active nanorod dimers for ultrasensitive dopamine detection. Biosens. Bioelectron. 2015, 71, 7–12.
  18. Gao, F.; Liu, L.; Cui, G.; Xu, L.; Wu, X.; Kuang, H.; Xu, C. Regioselective plasmonic nano-assemblies for bimodal sub-femtomolar dopamine detection. Nanoscale 2017, 9, 223–229.
  19. Lu, J.; Xu, C.; Nan, H.; Zhu, Q.; Qin, F.; Manohari, A.G.; Wei, M.; Zhu, Z.; Shi, Z.; Ni, Z. SERS-active ZnO/Ag hybrid WGM microcavity for ultrasensitive dopamine detection. Appl. Phys. Lett. 2016, 109, 073701.
  20. Lee, W.; Kang, B.-H.; Yang, H.; Park, M.; Kwak, J.H.; Chung, T.; Jeong, Y.; Kim, B.K.; Jeong, K.-H. Spread spectrum SERS allows label-free detection of attomolar neurotransmitters. Nat. Commun. 2021, 12, 159.
  21. Wang, P.; Xia, M.; Liang, O.; Sun, K.; Cipriano, A.F.; Schroeder, T.; Liu, H.; Xie, Y.-H. Label-Free SERS Selective Detection of Dopamine and Serotonin Using Graphene-Au Nanopyramid Heterostructure. Anal. Chem. 2015, 87, 10255–10261.
  22. Garre-Olmo, J. Epidemiology of Alzheimer’s disease and other dementias. Rev. Neurol. 2018, 66, 377–386.
  23. Du, Y.; Wu, H.T.; Qin, X.Y.; Cao, C.; Liu, Y.; Cao, Z.Z.; Cheng, Y. Postmortem Brain, Cerebrospinal Fluid, and Blood Neurotrophic Factor Levels in Alzheimer’s Disease: A Systematic Review and Meta-Analysis. J. Mol. Neurosci. 2018, 65, 289–300.
  24. Citron, M.; Oltersdorf, T.; Haass, C.; McConlogue, L.; Hung, A.Y.; Seubert, P.; Vigo-Pelfrey, C.; Lieberburg, I.; Selkoe, D.J. Mutation of the beta-amyloid precursor protein in familial Alzheimer’s disease increases beta-protein production. Nature 1992, 360, 672–674.
  25. Corder, E.H.; Saunders, A.M.; Strittmatter, W.J.; Schmechel, D.E.; Gaskell, P.C.; Small, G.W.; Roses, A.D.; Haines, J.L.; Pericak-Vance, M.A. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 1993, 261, 921–923.
  26. Bejanin, A.; Iulita, M.F.; Vilaplana, E.; Carmona-Iragui, M.; Benejam, B.; Videla, L.; Barroeta, I.; Fernandez, S.; Altuna, M.; Pegueroles, J.; et al. Association of Apolipoprotein E ɛ4 Allele with Clinical and Multimodal Biomarker Changes of Alzheimer Disease in Adults with Down Syndrome. JAMA Neurol 2021, 78, 937–947.
  27. Colautti, J.; Nagales, K. Tau and beta-amyloid in Alzheimer’s disease: Theories, treatments strategies, and future directions. The Meducator 2020, 1, 12–15.
  28. Goldgaber, D.; Lerman, M.I.; McBride, O.W.; Saffiotti, U.; Gajdusek, D.C. Characterization and chromosomal localization of a cDNA encoding brain amyloid of Alzheimer’s disease. Science 1987, 235, 877–880.
  29. Schmidt, S.D.; Mazzella, M.J.; Nixon, R.A.; Mathews, P.M. Aβ measurement by enzyme-linked immunosorbent assay. Methods Mol. Biol. 2012, 849, 507–527.
  30. Bharadwaj, P.; Waddington, L.; Varghese, J.; Macreadie, I.G. A new method to measure cellular toxicity of non-fibrillar and fibrillar Alzheimer’s Aβ using yeast. J. Alzheimers Dis. 2008, 13, 147–150.
  31. Russo, C.; Schettini, G.; Saido, T.C.; Hulette, C.; Lippa, C.; Lannfelt, L.; Ghetti, B.; Gambetti, P.; Tabaton, M.; Teller, J.K. Presenilin-1 mutations in Alzheimer’s disease. Nature 2000, 405, 531–532.
  32. Larner, A.J.; Doran, M. Clinical phenotypic heterogeneity of Alzheimer’s disease associated with mutations of the presenilin-1 gene. J. Neurol. 2006, 253, 139–158.
  33. Simon, D.K.; Tanner, C.M.; Brundin, P. Parkinson Disease Epidemiology, Pathology, Genetics, and Pathophysiology. Clin. Geriatr. Med. 2020, 36, 1–12.
  34. Lee, A.; Gilbert, R.M. Epidemiology of Parkinson Disease. Neurol. Clin. 2016, 34, 955–965.
  35. Armstrong, M.J.; Okun, M.S. Diagnosis and Treatment of Parkinson Disease: A Review. Jama 2020, 323, 548–560.
  36. Mahlknecht, P.; Hotter, A.; Hussl, A.; Esterhammer, R.; Schocke, M.; Seppi, K. Significance of MRI in diagnosis and differential diagnosis of Parkinson’s disease. Neurodegener. Dis. 2010, 7, 300–318.
  37. Smith, S.L.; Lones, M.A.; Bedder, M.; Alty, J.E.; Cosgrove, J.; Maguire, R.J.; Pownall, M.E.; Ivanoiu, D.; Lyle, C.; Cording, A.; et al. Computational approaches for understanding the diagnosis and treatment of Parkinson's disease. IET Syst. Biol. 2015, 9, 226–233.
  38. Hotter, A.; Esterhammer, R.; Schocke, M.F.; Seppi, K. Potential of advanced MR imaging techniques in the differential diagnosis of parkinsonism. Mov. Disord. 2009, 24 (Suppl. 2), S711–S720.
  39. Chahine, L.M.; Beach, T.G.; Brumm, M.C.; Adler, C.H.; Coffey, C.S.; Mosovsky, S.; Caspell-Garcia, C.; Serrano, G.E.; Munoz, D.G.; White, C.L.; et al. In vivo distribution of α-synuclein in multiple tissues and biofluids in Parkinson disease. Neurology 2020, 95, e1267–e1284.
  40. Wasner, K.; Grünewald, A.; Klein, C. Parkin-linked Parkinson’s disease: From clinical insights to pathogenic mechanisms and novel therapeutic approaches. Neurosci. Res. 2020, 159, 34–39.
  41. Nagatsu, T.; Nakashima, A.; Ichinose, H.; Kobayashi, K. Human tyrosine hydroxylase in Parkinson’s disease and in related disorders. J. Neural Transm. (Vienna) 2019, 126, 397–409.
  42. Rausch, W.D.; Wang, F.; Radad, K. From the tyrosine hydroxylase hypothesis of Parkinson’s disease to modern strategies: A short historical overview. J. Neural Transm. (Vienna) 2022, 129, 487–495.
  43. Kim, K.C.; Kim, P.; Go, H.S.; Choi, C.S.; Park, J.H.; Kim, H.J.; Jeon, S.J.; Pena, I.C.; Han, S.-H.; Cheong, J.H.; et al. Male-specific alteration in excitatory post-synaptic development and social interaction in pre-natal valproic acid exposure model of autism spectrum disorder. J. Neurochem. 2013, 124, 832–843.
  44. Tyzio, R.; Nardou, R.; Ferrari, D.C.; Tsintsadze, T.; Shahrokhi, A.; Eftekhari, S.; Khalilov, I.; Tsintsadze, V.; Brouchoud, C.; Chazal, G.; et al. Oxytocin-mediated GABA inhibition during delivery attenuates autism pathogenesis in rodent offspring. Science 2014, 343, 675–679.
  45. Gray, C. The Original Social Story Book; Jenison Public Schools: Jenison, MI, USA, 1993.
  46. Parellada, M.; Penzol, M.J.; Pina, L.; Moreno, C.; González-Vioque, E.; Zalsman, G.; Arango, C. The neurobiology of autism spectrum disorders. Eur. Psychiatry 2014, 29, 11–19.
  47. Arndt, T.L.; Stodgell, C.J.; Rodier, P.M. The teratology of autism. Int. J. Dev. Neurosci. 2005, 23, 189–199.
  48. Brun, L.; Auzias, G.; Viellard, M.; Villeneuve, N.; Girard, N.; Poinso, F.; Da Fonseca, D.; Deruelle, C. Localized Misfolding Within Broca’s Area as a Distinctive Feature of Autistic Disorder. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2016, 1, 160–168.
  49. Blackmon, K.; Ben-Avi, E.; Wang, X.; Pardoe, H.R.; Di Martino, A.; Halgren, E.; Devinsky, O.; Thesen, T.; Kuzniecky, R. Periventricular white matter abnormalities and restricted repetitive behavior in autism spectrum disorder. Neuroimage Clin. 2016, 10, 36–45.
  50. Li, D.; Karnath, H.-O.; Xu, X. Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies. Neurosci. Bull. 2017, 33, 219–237.
  51. Persico, A.M.; Napolioni, V. Autism genetics. Behav. Brain Res. 2013, 251, 95–112.
  52. Ruggeri, B.; Sarkans, U.; Schumann, G.; Persico, A.M. Biomarkers in autism spectrum disorder: The old and the new. Psychopharmacology 2014, 231, 1201–1216.
  53. Zafeiriou, D.I.; Ververi, A.; Dafoulis, V.; Kalyva, E.; Vargiami, E. Autism spectrum disorders: The quest for genetic syndromes. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2013, 162b, 327–366.
  54. Ford, T.C.; Crewther, D.P. A Comprehensive Review of the 1H-MRS Metabolite Spectrum in Autism Spectrum Disorder. Front. Mol. Neurosci. 2016, 9, 14.
  55. Anagnostou, E.; Taylor, M.J. Review of neuroimaging in autism spectrum disorders: What have we learned and where we go from here. Mol. Autism 2011, 2, 4.
  56. Ogruc Ildiz, G.; Bayari, S.; Karadag, A.; Kaygisiz, E.; Fausto, R. Fourier Transform Infrared Spectroscopy Based Complementary Diagnosis Tool for Autism Spectrum Disorder in Children and Adolescents. Molecules 2020, 25, 2079.
  57. Planchez, B.; Surget, A.; Belzung, C. Animal models of major depression: Drawbacks and challenges. J. Neural Transm. 2019, 126, 1383–1408.
  58. Shirayama, Y.; Yang, C.; Zhang, J.C.; Ren, Q.; Yao, W.; Hashimoto, K. Alterations in brain-derived neurotrophic factor (BDNF) and its precursor proBDNF in the brain regions of a learned helplessness rat model and the antidepressant effects of a TrkB agonist and antagonist. Eur. Neuropsychopharmacol. 2015, 25, 2449–2458.
  59. Dionisie, V.; Ciobanu, A.M.; Toma, V.A.; Manea, M.C.; Baldea, I.; Olteanu, D.; Sevastre-Berghian, A.; Clichici, S.; Manea, M.; Riga, S.; et al. Escitalopram Targets Oxidative Stress, Caspase-3, BDNF and MeCP2 in the Hippocampus and Frontal Cortex of a Rat Model of Depression Induced by Chronic Unpredictable Mild Stress. Int. J. Mol. Sci. 2021, 22, 7483.
  60. Smalheiser, N.R.; Lugli, G.; Rizavi, H.S.; Zhang, H.; Torvik, V.I.; Pandey, G.N.; Davis, J.M.; Dwivedi, Y. MicroRNA expression in rat brain exposed to repeated inescapable shock: Differential alterations in learned helplessness vs. non-learned helplessness. Int. J. Neuropsychopharmacol. 2011, 14, 1315–1325.
  61. Fiori, L.M.; Kos, A.; Lin, R.; Théroux, J.-F.; Lopez, J.P.; Kühne, C.; Eggert, C.; Holzapfel, M.; Huettl, R.-E.; Mechawar, N.; et al. miR-323a regulates ERBB4 and is involved in depression. Mol. Psychiatry 2021, 26, 4191–4204.
  62. Martins, H.C.; Schratt, G. MicroRNA-dependent control of neuroplasticity in affective disorders. Transl. Psychiatry 2021, 11, 263.
  63. Wallensten, J.; Nager, A.; Åsberg, M.; Borg, K.; Beser, A.; Wilczek, A.; Mobarrez, F. Leakage of astrocyte-derived extracellular vesicles in stress-induced exhaustion disorder: A cross-sectional study. Sci. Rep. 2021, 11, 2009.
  64. Flynn, S.; Leete, J.; Shahim, P.; Pattinson, C.; Guedes, V.A.; Lai, C.; Devoto, C.; Qu, B.-X.; Greer, K.; Moore, B.; et al. Extracellular vesicle concentrations of glial fibrillary acidic protein and neurofilament light measured 1 year after traumatic brain injury. Sci. Rep. 2021, 11, 3896.
  65. Bigio, B.; Dobbin, J.; Mathe, A.; Rasgon, N.; Nasca, C.; McEwen, B. Molecular Endophenotypes of Depression: From Computational Approaches to Exosome Biology. Biol. Psychiatry 2022, 91 (Suppl. S9), S28–S29.
  66. Osborne, L.M.; Payne, J.L.; Sherer, M.L.; Sabunciyan, S. Altered extracellular mRNA communication in postpartum depression is associated with decreased autophagy. Mol. Psychiatry 2022, 27, 4526–4535.
  67. Kumari, S.; Shivakumar, A.B.; Mehak, S.F.; Mazumder, N.; Gangadharan, G.; Pillai, V.G. Types of Raman Scattering Techniques for Neurodegenerative Diseases. In Advances in Brain Imaging Techniques; Mazumder, N., Gangadharan, G., Kistenev, Y.V., Eds.; Springer Nature Singapore: Singapore, 2022; pp. 39–57.
  68. Devitt, G.; Howard, K.; Mudher, A.; Mahajan, S. Raman Spectroscopy: An Emerging Tool in Neurodegenerative Disease Research and Diagnosis. ACS Chem. Neurosci. 2018, 9, 404–420.
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